Anomaly Warning and Fault Detection in DC Pico-grid with enhanced k-Nearest Neighbours Technique

Y. T. Quek, W. L. Woo, T. Logenthiran

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


The k-nearest neighbours (kNN) algorithm, which is usually used for classification, is presented in this paper to detect faults and trigger anomaly warnings in a single sensor multiple loads dc pico-grid. Anomalies warning is getting more attention in the recent years as it can used as a trigger for predictive maintenance, which is preferred over repair work after a fault detection. On top of performing its usual duty of load classification in the circuit during normal operation, the kNN algorithm is enhanced with 3 additional techniques to set 3 anomaly criteria for the triggering of alarm when the extracted features of the test object exhibit abnormal behaviours. The experiment is set in a dc pico-grid as there is a growing interest and demand in dc loads. Experiments with various anomalies show that the proposed enhanced algorithm can effectively detect anomalies and faults.
Original languageEnglish
Title of host publicationInternational Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
Number of pages6
ISBN (Electronic)9781538642917, 9781538642900
ISBN (Print)9781538642924
Publication statusPublished - 20 Sept 2018
Event2018 IEEE Innovative Smart Grid Technologies - Asia - Suntec Singapore International Convention and Exhibition Centre, Singapore, Singapore
Duration: 22 May 201825 May 2018


Conference2018 IEEE Innovative Smart Grid Technologies - Asia
Abbreviated titleISGT Asia 2018
Internet address


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